A suitable grid needs to provide physically relevant information not only to provide suitable inductive bias  [1]}, [2]}, but also to provide important information to practitioners. Inductive biases can be physical laws, assumptions, or human knowledge that can be inscribed into the data, model, and loss function to prioritize a specific solution and improve learning performance. Visually inspecting the grid should provide significant information to a scientist. The generalizability criterion is significant for using the grid for DL. As we have seen before, DL models using the same representation allow multiple different types of models to be used in conjunction. Also significantly, re-using learned models in transfer learning is imperative in low-data settings. Data availability in scientific research can be bimodal. There exist large datasets with millions and billions of samples as is the case for small organic and drug-like molecules. On the other hand novel, exotic materials may only have a hundred or thousand known samples. Therefore, a general representation that can cover many subdomains of material science is important. Finally, the invertibility of the representation is the ability to transform the structure to other canonical representations. First principle methods have long-established algorithms to quantify and classify materials. DL methods, especially generative models, are designed to augment the established scientific discovery pipeline. Therefore the data representation requires interoperability between representations suited for DL and representations required by other computational and experimental methods.
